{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/convolution/papers/80","list_of":"/method/convolution","method":"Convolution","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":80,"pages_in_order":196,"rows_per_page":100,"rows":[7901,8000],"of":19586,"counts":{"archive_papers_tagged":19586,"with_a_code_link":8064,"where_syntology_ran_a_sample":1837,"not_listed_spam_title":0,"listed":19586,"listed_where_code_ran":1837,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1557,"every_run_a_failure_of_syntologys_instrument":280,"listed_with_a_run_with_no_instrument_failure":1557,"listed_every_run_a_failure_of_syntologys_instrument":280,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/convolution","prev":"/method/convolution/papers/79","next":"/method/convolution/papers/81","papers":[{"paper":null,"slug":"novel-multicolumn-kernel-extreme-learning","title":"Novel Multicolumn Kernel Extreme Learning Machine for Food Detection via Optimal Features from CNN","date":"2022-05-15","arxiv_id":"2205.07348","n_code_links":0,"syntology":null},{"paper":"/paper/optimization-of-decision-tree-evaluation","slug":"optimization-of-decision-tree-evaluation","title":"Optimization of Decision Tree Evaluation Using SIMD Instructions","date":"2022-05-15","arxiv_id":"2205.07307","n_code_links":1,"syntology":null},{"paper":null,"slug":"superwarp-supervised-learning-and-warping-on","title":"SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration","date":"2022-05-15","arxiv_id":"2205.07399","n_code_links":0,"syntology":null},{"paper":"/paper/video-frame-interpolation-with-transformer","slug":"video-frame-interpolation-with-transformer","title":"Video Frame Interpolation with Transformer","date":"2022-05-15","arxiv_id":"2205.07230","n_code_links":1,"syntology":null},{"paper":"/paper/classification-of-astronomical-bodies-by","slug":"classification-of-astronomical-bodies-by","title":"Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural Networks","date":"2022-05-14","arxiv_id":"2205.07124","n_code_links":1,"syntology":null},{"paper":null,"slug":"dense-residual-transformer-for-image","title":"Dense residual Transformer for image denoising","date":"2022-05-14","arxiv_id":"2205.06944","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-deep-learning-methods-for","slug":"efficient-deep-learning-methods-for","title":"Efficient Deep Learning Methods for Identification of Defective Casting Products","date":"2022-05-14","arxiv_id":"2205.07118","n_code_links":1,"syntology":null},{"paper":null,"slug":"low-power-communication-signal-enhancement","title":"Low power communication signal enhancement method of Internet of things based on nonlocal mean denoising","date":"2022-05-14","arxiv_id":"2205.10323","n_code_links":0,"syntology":null},{"paper":"/paper/mask-cyclegan-unpaired-multi-modal-domain","slug":"mask-cyclegan-unpaired-multi-modal-domain","title":"Mask CycleGAN: Unpaired Multi-modal Domain Translation with Interpretable Latent Variable","date":"2022-05-14","arxiv_id":"2205.06969","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-modal-curb-detection-and-filtering","title":"Multi-modal curb detection and filtering","date":"2022-05-14","arxiv_id":"2205.07096","n_code_links":0,"syntology":null},{"paper":null,"slug":"qhd-a-brain-inspired-hyperdimensional","title":"Efficient Off-Policy Reinforcement Learning via Brain-Inspired Computing","date":"2022-05-14","arxiv_id":"2205.06978","n_code_links":0,"syntology":null},{"paper":null,"slug":"revisiting-facial-key-point-detection-an","title":"Revisiting Facial Key Point Detection: An Efficient Approach Using Deep Neural Networks","date":"2022-05-14","arxiv_id":"2205.07121","n_code_links":0,"syntology":null},{"paper":null,"slug":"analysis-of-neural-image-compression-networks","title":"Analysis of Neural Image Compression Networks for Machine-to-Machine Communication","date":"2022-05-13","arxiv_id":"2205.06511","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-deep-neural-object-detection-and","title":"Robust Deep Neural Object Detection and Segmentation for Automotive Driving Scenario with Compressed Image Data","date":"2022-05-13","arxiv_id":"2205.06501","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-equivalence-principle-for-the-spectrum-of","title":"An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings","date":"2022-05-12","arxiv_id":"2205.06308","n_code_links":0,"syntology":null},{"paper":"/paper/blueprint-separable-residual-network-for","slug":"blueprint-separable-residual-network-for","title":"Blueprint Separable Residual Network for Efficient Image Super-Resolution","date":"2022-05-12","arxiv_id":"2205.05996","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":4,"n_instrument":2,"unverified":1,"pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["xiaom233/bsrn"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"enhanced-single-shot-detector-for-small","title":"Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images","date":"2022-05-12","arxiv_id":"2205.05927","n_code_links":0,"syntology":null},{"paper":"/paper/group-r-cnn-for-weakly-semi-supervised-object","slug":"group-r-cnn-for-weakly-semi-supervised-object","title":"Group R-CNN for Weakly Semi-supervised Object Detection with Points","date":"2022-05-12","arxiv_id":"2205.05920","n_code_links":1,"syntology":null},{"paper":null,"slug":"infrared-invisible-clothing-hiding-from","title":"Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World","date":"2022-05-12","arxiv_id":"2205.05909","n_code_links":0,"syntology":null},{"paper":null,"slug":"overparameterization-improves-stylegan","title":"Overparameterization Improves StyleGAN Inversion","date":"2022-05-12","arxiv_id":"2205.06304","n_code_links":0,"syntology":null},{"paper":null,"slug":"unified-source-filter-gan-with-harmonic-plus","title":"Unified Source-Filter GAN with Harmonic-plus-Noise Source Excitation Generation","date":"2022-05-12","arxiv_id":"2205.06053","n_code_links":0,"syntology":null},{"paper":"/paper/aggpose-deep-aggregation-vision-transformer","slug":"aggpose-deep-aggregation-vision-transformer","title":"AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation","date":"2022-05-11","arxiv_id":"2205.05277","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":5,"n_instrument":0,"unverified":2,"pointer_only":7,"phrase":"5 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","official":{"repos":["szar-lab/aggpose"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/an-empirical-study-of-self-supervised","slug":"an-empirical-study-of-self-supervised","title":"An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers","date":"2022-05-11","arxiv_id":"2205.05543","n_code_links":2,"syntology":null},{"paper":null,"slug":"characterizing-the-action-generalization-gap","title":"Characterizing the Action-Generalization Gap in Deep Q-Learning","date":"2022-05-11","arxiv_id":"2205.05588","n_code_links":0,"syntology":null},{"paper":null,"slug":"invisible-to-visible-privacy-aware-human-1","title":"Invisible-to-Visible: Privacy-Aware Human Segmentation using Airborne Ultrasound via Collaborative Learning Probabilistic U-Net","date":"2022-05-11","arxiv_id":"2205.05293","n_code_links":0,"syntology":null},{"paper":null,"slug":"repsr-training-efficient-vgg-style-super","title":"RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization","date":"2022-05-11","arxiv_id":"2205.05671","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatial-temporal-associations-representation","title":"Spatial-temporal associations representation and application for process monitoring using graph convolution neural network","date":"2022-05-11","arxiv_id":"2205.05250","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-spatial-temporal-short-term-traffic-flow","title":"A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism","date":"2022-05-10","arxiv_id":"2205.04762","n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerating-the-training-of-video-super","title":"Accelerating the Training of Video Super-Resolution Models","date":"2022-05-10","arxiv_id":"2205.05069","n_code_links":0,"syntology":null},{"paper":null,"slug":"flow-completion-network-inferring-the-fluid","title":"Flow Completion Network: Inferring the Fluid Dynamics from Incomplete Flow Information using Graph Neural Networks","date":"2022-05-10","arxiv_id":"2205.04739","n_code_links":0,"syntology":null},{"paper":null,"slug":"hybrid-reinforcement-learning-for-star-riss-a","title":"Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer","date":"2022-05-10","arxiv_id":"2205.05029","n_code_links":0,"syntology":null},{"paper":"/paper/identical-image-retrieval-using-deep-learning","slug":"identical-image-retrieval-using-deep-learning","title":"Identical Image Retrieval using Deep Learning","date":"2022-05-10","arxiv_id":"2205.04883","n_code_links":1,"syntology":null},{"paper":null,"slug":"object-detection-in-indian-food-platters","title":"Object Detection in Indian Food Platters using Transfer Learning with YOLOv4","date":"2022-05-10","arxiv_id":"2205.04841","n_code_links":0,"syntology":null},{"paper":"/paper/shadow-aware-dynamic-convolution-for-shadow","slug":"shadow-aware-dynamic-convolution-for-shadow","title":"Shadow-Aware Dynamic Convolution for Shadow Removal","date":"2022-05-10","arxiv_id":"2205.04908","n_code_links":2,"syntology":null},{"paper":null,"slug":"smartsage-training-large-scale-graph-neural","title":"SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures","date":"2022-05-10","arxiv_id":"2205.04711","n_code_links":0,"syntology":null},{"paper":"/paper/the-impact-of-partial-occlusion-on-pedestrian","slug":"the-impact-of-partial-occlusion-on-pedestrian","title":"The Impact of Partial Occlusion on Pedestrian Detectability","date":"2022-05-10","arxiv_id":"2205.04812","n_code_links":2,"syntology":null},{"paper":null,"slug":"using-frequency-attention-to-make-adversarial","title":"Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector","date":"2022-05-10","arxiv_id":"2205.04638","n_code_links":0,"syntology":null},{"paper":null,"slug":"alternative-data-augmentation-for-industrial","title":"Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning","date":"2022-05-09","arxiv_id":"2205.04222","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-effective-scheme-for-maize-disease","title":"An Effective Scheme for Maize Disease Recognition based on Deep Networks","date":"2022-05-09","arxiv_id":"2205.04234","n_code_links":0,"syntology":null},{"paper":null,"slug":"hardware-robust-in-rram-computing-for-object","title":"Hardware-Robust In-RRAM-Computing for Object Detection","date":"2022-05-09","arxiv_id":"2205.03996","n_code_links":0,"syntology":null},{"paper":"/paper/hierattn-effectively-learn-representations","slug":"hierattn-effectively-learn-representations","title":"Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention","date":"2022-05-09","arxiv_id":"2205.04326","n_code_links":2,"syntology":null},{"paper":null,"slug":"incremental-detr-incremental-few-shot-object","title":"Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning","date":"2022-05-09","arxiv_id":"2205.04042","n_code_links":0,"syntology":null},{"paper":null,"slug":"layoutxlm-vs-gnn-an-empirical-evaluation-of","title":"LayoutXLM vs. GNN: An Empirical Evaluation of Relation Extraction for Documents","date":"2022-05-09","arxiv_id":"2206.10304","n_code_links":0,"syntology":null},{"paper":null,"slug":"lstm-based-distributed-conditional-generative","title":"LSTM-Based Distributed Conditional Generative Adversarial Network For Data-Driven 5G-Enabled Maritime UAV Communications","date":"2022-05-09","arxiv_id":"2205.04196","n_code_links":0,"syntology":null},{"paper":"/paper/object-detection-with-spiking-neural-networks","slug":"object-detection-with-spiking-neural-networks","title":"Object Detection with Spiking Neural Networks on Automotive Event Data","date":"2022-05-09","arxiv_id":"2205.04339","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["loiccordone/object-detection-with-spiking-neural-networks"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/site-generalization-stroke-lesion","slug":"site-generalization-stroke-lesion","title":"SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization","date":"2022-05-09","arxiv_id":"2205.04329","n_code_links":1,"syntology":null},{"paper":null,"slug":"transem-residual-swin-transformer-based","title":"TransEM:Residual Swin-Transformer based regularized PET image reconstruction","date":"2022-05-09","arxiv_id":"2205.04204","n_code_links":0,"syntology":null},{"paper":"/paper/a-nas-neural-architecture-search-using","slug":"a-nas-neural-architecture-search-using","title":"Neural Architecture Search using Property Guided Synthesis","date":"2022-05-08","arxiv_id":"2205.03960","n_code_links":1,"syntology":null},{"paper":"/paper/convmae-masked-convolution-meets-masked","slug":"convmae-masked-convolution-meets-masked","title":"ConvMAE: Masked Convolution Meets Masked Autoencoders","date":"2022-05-08","arxiv_id":"2205.03892","n_code_links":5,"syntology":{"ran":10,"of":10,"n_ran_checked":10,"n_instrument":0,"unverified":0,"pointer_only":4,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["alpha-vl/convmae"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"deep-embedded-multi-view-clustering-via","title":"Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs","date":"2022-05-08","arxiv_id":"2205.03803","n_code_links":0,"syntology":null},{"paper":"/paper/from-heavy-rain-removal-to-detail-restoration","slug":"from-heavy-rain-removal-to-detail-restoration","title":"From heavy rain removal to detail restoration: A faster and better network","date":"2022-05-07","arxiv_id":"2205.03553","n_code_links":1,"syntology":null},{"paper":"/paper/ultra-fast-image-categorization-in-vivo-and","slug":"ultra-fast-image-categorization-in-vivo-and","title":"Ultrafast Image Categorization in Biology and Neural Models","date":"2022-05-07","arxiv_id":"2205.03635","n_code_links":1,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-non-blind-deblurring","title":"Comparative Analysis of Non-Blind Deblurring Methods for Noisy Blurred Images","date":"2022-05-06","arxiv_id":"2205.03464","n_code_links":0,"syntology":null},{"paper":null,"slug":"generate-and-edit-your-own-character-in-a","title":"Generate and Edit Your Own Character in a Canonical View","date":"2022-05-06","arxiv_id":"2205.02974","n_code_links":0,"syntology":null},{"paper":null,"slug":"rcmnet-a-deep-learning-model-assists-car-t","title":"RCMNet: A deep learning model assists CAR-T therapy for leukemia","date":"2022-05-06","arxiv_id":"2205.04230","n_code_links":0,"syntology":null},{"paper":"/paper/sound2synth-interpreting-sound-via-fm","slug":"sound2synth-interpreting-sound-via-fm","title":"Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation","date":"2022-05-06","arxiv_id":"2205.03043","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-nt-xent-loss-upper-bound","title":"The NT-Xent loss upper bound","date":"2022-05-06","arxiv_id":"2205.03169","n_code_links":0,"syntology":null},{"paper":null,"slug":"trainable-wavelet-neural-network-for-non","title":"Trainable Wavelet Neural Network for Non-Stationary Signals","date":"2022-05-06","arxiv_id":"2205.03355","n_code_links":0,"syntology":null},{"paper":"/paper/are-gan-based-morphs-threatening-face","slug":"are-gan-based-morphs-threatening-face","title":"Are GAN-based Morphs Threatening Face Recognition?","date":"2022-05-05","arxiv_id":"2205.02496","n_code_links":1,"syntology":null},{"paper":null,"slug":"biologically-inspired-deep-residual-networks","title":"Biologically inspired deep residual networks for computer vision applications","date":"2022-05-05","arxiv_id":"2205.02551","n_code_links":0,"syntology":null},{"paper":null,"slug":"blobgan-spatially-disentangled-scene","title":"BlobGAN: Spatially Disentangled Scene Representations","date":"2022-05-05","arxiv_id":"2205.02837","n_code_links":0,"syntology":null},{"paper":"/paper/declaration-based-prompt-tuning-for-visual","slug":"declaration-based-prompt-tuning-for-visual","title":"Declaration-based Prompt Tuning for Visual Question Answering","date":"2022-05-05","arxiv_id":"2205.02456","n_code_links":1,"syntology":null},{"paper":"/paper/dual-octree-graph-networks-for-learning","slug":"dual-octree-graph-networks-for-learning","title":"Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representations","date":"2022-05-05","arxiv_id":"2205.02825","n_code_links":1,"syntology":{"ran":5,"of":6,"n_ran_checked":5,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["microsoft/DualOctreeGNN"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"generative-adversarial-network-based-3","title":"Generative Adversarial Network Based Synthetic Learning and a Novel Domain Relevant Loss Term for Spine Radiographs","date":"2022-05-05","arxiv_id":"2205.02843","n_code_links":0,"syntology":null},{"paper":null,"slug":"invariant-content-synergistic-learning-for","title":"Invariant Content Synergistic Learning for Domain Generalization of Medical Image Segmentation","date":"2022-05-05","arxiv_id":"2205.02845","n_code_links":0,"syntology":null},{"paper":null,"slug":"mminr-multi-frame-to-multi-frame-inference","title":"MMINR: Multi-frame-to-Multi-frame Inference with Noise Resistance for Precipitation Nowcasting with Radar","date":"2022-05-05","arxiv_id":"2205.02457","n_code_links":0,"syntology":null},{"paper":null,"slug":"st-expertnet-a-deep-expert-framework-for","title":"ST-ExpertNet: A Deep Expert Framework for Traffic Prediction","date":"2022-05-05","arxiv_id":"2205.07851","n_code_links":0,"syntology":null},{"paper":"/paper/text-to-artistic-image-generation","slug":"text-to-artistic-image-generation","title":"Text to artistic image generation","date":"2022-05-05","arxiv_id":"2205.02439","n_code_links":1,"syntology":null},{"paper":null,"slug":"deeptdcs-deep-learning-based-estimation-of","title":"DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation","date":"2022-05-04","arxiv_id":"2205.01858","n_code_links":0,"syntology":null},{"paper":null,"slug":"domino-saliency-metrics-improving-existing","title":"Domino Saliency Metrics: Improving Existing Channel Saliency Metrics with Structural Information","date":"2022-05-04","arxiv_id":"2205.02131","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-transferability-for-covid-3d","title":"Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models","date":"2022-05-04","arxiv_id":"2205.02152","n_code_links":0,"syntology":null},{"paper":"/paper/geocentric-pose-analysis-of-satellite-imagery","slug":"geocentric-pose-analysis-of-satellite-imagery","title":"A Deep Learning Ensemble Framework for Off-Nadir Geocentric Pose Prediction","date":"2022-05-04","arxiv_id":"2205.11230","n_code_links":1,"syntology":null},{"paper":"/paper/probabilistic-symmetry-for-improved","slug":"probabilistic-symmetry-for-improved","title":"Probabilistic Symmetry for Multi-Agent Dynamics","date":"2022-05-04","arxiv_id":"2205.01927","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["rose-stl-lab/pecco"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":null,"slug":"unsupervised-domain-adaptation-learning-for","title":"Unsupervised Domain Adaptation Learning for Hierarchical Infant Pose Recognition with Synthetic Data","date":"2022-05-04","arxiv_id":"2205.01892","n_code_links":0,"syntology":null},{"paper":"/paper/bioculargan-bimodal-synthesis-and-annotation","slug":"bioculargan-bimodal-synthesis-and-annotation","title":"BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images","date":"2022-05-03","arxiv_id":"2205.01536","n_code_links":1,"syntology":null},{"paper":null,"slug":"data-consistent-non-cartesian-deep-subspace","title":"Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction","date":"2022-05-03","arxiv_id":"2205.01770","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-multi-scale-u-net-architecture-and-noise","title":"Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation","date":"2022-05-03","arxiv_id":"2205.01777","n_code_links":0,"syntology":null},{"paper":null,"slug":"effect-of-random-histogram-equalization-on","title":"Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning","date":"2022-05-03","arxiv_id":"2205.01684","n_code_links":0,"syntology":null},{"paper":"/paper/in-defense-of-image-pre-training-for","slug":"in-defense-of-image-pre-training-for","title":"In Defense of Image Pre-Training for Spatiotemporal Recognition","date":"2022-05-03","arxiv_id":"2205.01721","n_code_links":1,"syntology":null},{"paper":"/paper/masked-generative-distillation","slug":"masked-generative-distillation","title":"Masked Generative Distillation","date":"2022-05-03","arxiv_id":"2205.01529","n_code_links":3,"syntology":null},{"paper":null,"slug":"multi-scale-sparse-convolution-point-cloud","title":"Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution Neural Network","date":"2022-05-03","arxiv_id":"2205.01550","n_code_links":0,"syntology":null},{"paper":null,"slug":"outdoor-monocular-depth-estimation-a-research","title":"Outdoor Monocular Depth Estimation: A Research Review","date":"2022-05-03","arxiv_id":"2205.01399","n_code_links":0,"syntology":null},{"paper":null,"slug":"spinenetv2-automated-detection-labelling-and","title":"SpineNetV2: Automated Detection, Labelling and Radiological Grading Of Clinical MR Scans","date":"2022-05-03","arxiv_id":"2205.01683","n_code_links":0,"syntology":null},{"paper":null,"slug":"synthesized-speech-detection-using","title":"Synthesized Speech Detection Using Convolutional Transformer-Based Spectrogram Analysis","date":"2022-05-03","arxiv_id":"2205.01800","n_code_links":0,"syntology":null},{"paper":"/paper/when-multi-level-meets-multi-interest-a-multi","slug":"when-multi-level-meets-multi-interest-a-multi","title":"When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation","date":"2022-05-03","arxiv_id":"2205.01286","n_code_links":1,"syntology":{"ran":2,"of":6,"n_ran_checked":2,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["whuir/mgnm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"3d-convolutional-neural-networks-for-dendrite","title":"3D Convolutional Neural Networks for Dendrite Segmentation Using Fine-Tuning and Hyperparameter Optimization","date":"2022-05-02","arxiv_id":"2205.01167","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-performance-consistent-and-computation","title":"A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation","date":"2022-05-02","arxiv_id":"2205.01239","n_code_links":0,"syntology":null},{"paper":null,"slug":"bsra-block-based-super-resolution-accelerator","title":"BSRA: Block-based Super Resolution Accelerator with Hardware Efficient Pixel Attention","date":"2022-05-02","arxiv_id":"2205.00777","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-framework-for-real-time-fetal","slug":"deep-learning-framework-for-real-time-fetal","title":"Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI","date":"2022-05-02","arxiv_id":"2205.01675","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-accelerator-for-dilated-and","title":"Efficient Accelerator for Dilated and Transposed Convolution with Decomposition","date":"2022-05-02","arxiv_id":"2205.02103","n_code_links":0,"syntology":null},{"paper":"/paper/harmof0-logarithmic-scale-dilated-convolution","slug":"harmof0-logarithmic-scale-dilated-convolution","title":"HarmoF0: Logarithmic Scale Dilated Convolution For Pitch Estimation","date":"2022-05-02","arxiv_id":"2205.01019","n_code_links":1,"syntology":null},{"paper":null,"slug":"leaf-tar-spot-detection-using-rgb-images","title":"Leaf Tar Spot Detection Using RGB Images","date":"2022-05-02","arxiv_id":"2205.00952","n_code_links":0,"syntology":null},{"paper":null,"slug":"lightweight-image-enhancement-network-for","title":"Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation","date":"2022-05-02","arxiv_id":"2205.00853","n_code_links":0,"syntology":null},{"paper":"/paper/nha12d-a-new-pavement-crack-dataset-and-a","slug":"nha12d-a-new-pavement-crack-dataset-and-a","title":"NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack Detection Algorithms","date":"2022-05-02","arxiv_id":"2205.01198","n_code_links":1,"syntology":null},{"paper":null,"slug":"pscnn-a-885-86-tops-w-programmable-sram-based","title":"PSCNN: A 885.86 TOPS/W Programmable SRAM-based Computing-In-Memory Processor for Keyword Spotting","date":"2022-05-02","arxiv_id":"2205.01569","n_code_links":0,"syntology":null},{"paper":null,"slug":"real-time-on-sensor-gait-phase-detection-with","title":"Real Time On Sensor Gait Phase Detection with 0.5KB Deep Learning Model","date":"2022-05-02","arxiv_id":"2205.03234","n_code_links":0,"syntology":null},{"paper":"/paper/augmented-balanced-image-dataset-generator","slug":"augmented-balanced-image-dataset-generator","title":"Augmented Balanced Image Dataset Generator Using AugStatic Library","date":"2022-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"engineering-deep-learning-methods-on","title":"Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events","date":"2022-05-01","arxiv_id":"2205.02125","n_code_links":0,"syntology":null},{"paper":"/paper/improving-model-performance-and-removing-the","slug":"improving-model-performance-and-removing-the","title":"Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation","date":"2022-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/reinforced-swin-convs-transformer-for","slug":"reinforced-swin-convs-transformer-for","title":"Reinforced Swin-Convs Transformer for Underwater Image Enhancement","date":"2022-05-01","arxiv_id":"2205.00434","n_code_links":1,"syntology":{"ran":4,"of":8,"n_ran_checked":4,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","official":{"repos":["TingdiRen/URSCT-SESR"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}}],"record_sha256":"1170cd6784cde83254622f186b9aa84f3744e9dbd2e3073267aaf2e92ee4b42e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}